Method and device for determining residence point, equipment, medium and program product

Abstract

The embodiment of the invention provides a method and device for determining a residence point, equipment, a medium and a program product. The method comprises the following steps: acquiring mobile phone signaling data of a user; the mobile phone signaling data comprises user identity information, signaling access time and position information of a base station corresponding to the signaling; acquiring the base station density; determining a core object in the mobile phone signaling data of the user according to the base station density, and generating a cluster; and taking the geometric center in the clustering cluster as a resident point. The method is used for achieving the effect of obtaining the residence point more accurately.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, equipment, medium and program product for determining a residence point. Background Art

[0002] In the field of mobile communications, a dwell point usually refers to the base station to which a mobile phone is currently connected. By understanding the location of a user's dwell point, it is helpful to understand the user's activity patterns, and thus to better deploy network infrastructure. With the popularity of mobile communication devices such as smartphones, smart homes, and smart wearable devices, communication operators have accumulated a huge amount of signaling data. By analyzing the signaling data, the status and location of the mobile phone can be understood and tracked.

[0003] The existing solution directly pre-clusters the spatial information of mobile phone signaling data and processes it in combination with time sorting to obtain the final dwelling point. The accuracy of the dwelling point obtained in this way is low. Summary of the invention

[0004] The embodiments of the present application provide a method, apparatus, device, medium and program product for determining a dwell point, so as to achieve a more accurate effect of obtaining a dwell point.

[0005] In a first aspect, an embodiment of the present application provides a method for determining a dwell point, comprising:

[0006] Acquire the user's mobile phone signaling data; the mobile phone signaling data includes the user's identity information, the access time of the signaling, and the location information of the base station corresponding to the signaling;

[0007] Get base station density;

[0008] Determine core objects in the mobile phone signaling data of the user according to the base station density, and generate clusters;

[0009] The geometric center of the cluster is taken as the residence point.

[0010] In a possible implementation, the obtaining of the user's mobile phone signaling data includes:

[0011] Acquire full data, where the full data includes all signaling data of the user within a preset time period;

[0012] Performing data cleaning on the full amount of data;

[0013] The cleaned full amount of data is filled with data and / or simplified to obtain the mobile phone signaling data of the user.

[0014] In a possible implementation, the step of cleaning the full amount of data includes:

[0015] sorting the plurality of signaling data in the full amount of data according to access time;

[0016] Calculate the distance and time difference between adjacent signaling data to obtain the moving speed of the next signaling data;

[0017] The signaling data with a moving speed greater than a preset speed threshold is deleted from the full amount of data.

[0018] In a possible implementation manner, the step of performing data filling on the cleaned full amount of data includes:

[0019] Sort the signaling data in the cleaned full data according to the access time;

[0020] If the time difference between adjacent signaling data is greater than a first preset time threshold and corresponds to the same base station, data filling is performed between the adjacent signaling data according to a first preset time interval.

[0021] In a possible implementation manner, obtaining the base station density includes:

[0022] Obtaining the number of base stations in at least one preset area;

[0023] Determine the weights corresponding to the preset areas according to the distances between the boundaries of the preset areas and the target base station, wherein the weights are inversely proportional to the distances, and the target base station is any base station within the preset areas;

[0024] The base station density of the target base station is determined according to each of the weights and the number of each of the base stations.

[0025] In a possible implementation manner, determining core objects in the mobile phone signaling data of the user according to the base station density and generating clusters includes:

[0026] Adjusting a preset neighborhood parameter value based on the base station density;

[0027] All core objects are found in the mobile phone signaling data of the user according to the adjusted neighborhood parameter values, and clusters are generated.

[0028] In a possible implementation manner, finding all core objects in the mobile phone signaling data of the user according to the adjusted neighborhood parameter value and generating clusters includes:

[0029] Normalizing the mobile phone signaling data of the user to obtain a sample set corresponding to the user;

[0030] Determine all core objects in the sample set according to the adjusted neighborhood parameter values;

[0031] For each core object in the sample set, find the samples whose density is reachable in the sample set, and generate clusters corresponding to each core object.

[0032] In a possible implementation manner, after taking the geometric center of the cluster as the stationary point, the method further includes:

[0033] The residence time of the residence point is obtained by subtracting the minimum access time in the cluster from the maximum access time in the cluster.

[0034] In a second aspect, an embodiment of the present application provides a device for determining a dwell point, including:

[0035] A first acquisition module is used to acquire the user's mobile phone signaling data; the mobile phone signaling data includes the user's identity information, the access time of the signaling, and the location information of the base station corresponding to the signaling;

[0036] A second acquisition module is used to acquire base station density;

[0037] A user trajectory clustering module, used to determine core objects in the mobile phone signaling data of the user according to the base station density, and generate clusters;

[0038] The resident information marking module is used to use the geometric center of the cluster as the resident point.

[0039] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor;

[0040] The memory stores computer-executable instructions;

[0041] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0042] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0043] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0044] The method, apparatus, device, medium and program product for determining the dwell point provided in the embodiments of the present application obtain the user's mobile phone signaling data and base station density, and then determine the core object in the mobile phone signaling data according to the base station density, thereby generating a cluster, and finally determining the geometric center of the cluster as the dwell point. In this way, the influence of the base station density on the mobile phone signaling data can be considered, and the mobile phone signaling data can be clustered based on the base station density, so as to achieve a more accurate effect of determining the dwell point. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0046] Figure 1 A schematic diagram of a flow chart of a method for determining a dwell point provided in the present application;

[0047] Figure 2 A schematic diagram of the relationship between the weight and distance corresponding to a preset area provided in this application;

[0048] FIG3( a) is a flow chart of a method for obtaining mobile phone signaling data provided by the present application;

[0049] FIG3( b) is a schematic diagram of the principle of a method for determining a dwell point provided by the present application;

[0050] Figure 4 A schematic diagram of the structure of a device for determining a dwell point provided in the present application;

[0051] Figure 5 A schematic diagram of the structure of the electronic device provided in this application.

[0052] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0053] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0054] Base stations are key devices in mobile communication networks, responsible for providing wireless network coverage to the corresponding coverage area and communicating with mobile devices such as mobile phones.

[0055] The coverage area corresponding to the base station is an area formed with the base station as the center and the first preset length as the radius, and each base station corresponds to a coverage area.

[0056] Mobile signaling data refers to various control information generated in the mobile communication network to manage and control the communication process between base stations and mobile devices. These data record the interaction between user equipment and the network, including but not limited to location updates, call establishment, SMS sending and receiving, data transmission, etc. Mobile signaling data is generated in the network of telecommunications operators.

[0057] Therefore, by analyzing mobile phone signaling data, we can understand the location of users' residence points, thereby analyzing the patterns of users' activities, and thus better deploy network infrastructure.

[0058] The existing scheme usually directly pre-cluster the spatial information of mobile phone signaling data, and process it in combination with time sorting to obtain the final residence point. However, the existing scheme does not consider the impact of base station density on the residence point. Base station density refers to the number of base stations per unit area in a specific geographical area. Specifically, in low base station density areas, the coverage area corresponding to a base station may cover a range of several kilometers, while in high base station density areas, the coverage area corresponding to a base station may only cover a few hundred meters. Among them, the spatial distribution of mobile phone signaling data is greatly affected by the base station density. In dense base station areas, mobile devices will frequently switch coverage areas, resulting in a large number of short-term residence points. These short-term residence points may be mistaken for real residence points, thereby generating noise. In sparse base station areas, due to the sparse signaling data, it may not provide enough information for effective clustering. This leads to an increase in the error of the clustering result. Therefore, without considering the base station density, directly pre-clustering the spatial information of mobile phone signaling data is likely to result in a low accuracy of the final determined residence point.

[0059] In order to provide accuracy in identifying the dwelling point, the present application provides a method for determining the dwelling point. After obtaining the mobile phone signaling data, the base station density is obtained, and then the mobile phone signaling data is clustered in combination with the base station density, so as to determine the dwelling point from the mobile phone signaling data. In this way, the influence of the base station density on the mobile phone signaling data is taken into account, so as to achieve a more accurate effect of obtaining the dwelling point.

[0060] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0061] Figure 1 A flow chart of a method for determining a dwell point provided in the present application is shown in the figure, and the method includes:

[0062] Step S101, obtaining the user's mobile phone signaling data; the mobile phone signaling data includes the user's identity information, the access time of the signaling, and the location information of the base station corresponding to the signaling.

[0063] As the user moves, the mobile device will gradually enter the preset areas corresponding to different base stations. When the mobile device enters the preset area corresponding to a new base station, it will send a registration request to the nearest base station. This process will generate mobile signaling data to record the location and status of the mobile device.

[0064] The mobile phone signaling data of a user may include multiple signalings, each of which may correspond to different base stations. That is, when the mobile device enters the coverage areas corresponding to different base stations, it will send signalings to different base stations to obtain the signaling data corresponding to different base stations.

[0065] Step S102, obtaining base station density.

[0066] Specifically, the number of base stations in at least one preset area is obtained; the weights corresponding to each preset area are determined according to the distance between the boundary of each preset area and the target base station, the weight is inversely proportional to the distance, and the target base station is any base station in the preset area; the base station density of the target base station is determined based on each weight and the number of base stations.

[0067] Furthermore, the core point may be determined first, and then multiple preset areas may be delineated with the core point as the center and multiple different second preset lengths as the radius. The core point may be the location information of a base station corresponding to any signal in the mobile phone signaling book.

[0068] The weight corresponding to the i-th preset area satisfies the formula x i Indicates the distance between the i-th preset area and the target base station.

[0069] In some embodiments, the target base station is, for example, base station a, and the preset areas include four, namely, the first area, the second area, the third area, and the fourth area. The radius of the first area is 150 meters. The radius of the second area is 250 meters. The radius of the third area is 500 meters, and the radius of the fourth area is 1000 meters. Then the base station density of base station a satisfies the following formula ρ a =w1×n1+w2×n2+w3×n3+w4×n4. Where n1 represents the total number of base stations in the first area, n2 represents the total number of base stations in the second area, n3 represents the total number of base stations in the third area, and n4 represents the total number of base stations in the fourth area. w1 represents the weight corresponding to the first area, w2 represents the weight corresponding to the second area, w3 represents the weight corresponding to the third area, and w4 represents the weight corresponding to the fourth area. Figure 2 The corresponding relationship diagram between the weight and the distance shown in the figure has a vertical axis representing the weight of the preset area and a horizontal axis representing the distance between the preset area and the base station a. It can be seen that the weight of the preset area is inversely proportional to the distance between the preset area and the base station a.

[0070] Step S103: determining core objects in the user's mobile phone signaling data according to the base station density, and generating clusters.

[0071] Specifically, the preset neighborhood parameter value is adjusted based on the base station density; all core objects are found in the user's mobile phone signaling data according to the adjusted neighborhood parameter value, and clusters are generated.

[0072] In some embodiments, the The adjusted neighborhood parameter value corresponding to base station a is obtained. The greater the density, the smaller the adjusted neighborhood parameter value, that is, the smaller the search range. ε is the preset neighborhood parameter value.

[0073] Furthermore, core objects are determined in the user's mobile phone signaling data according to the adjusted neighborhood parameter values, and clusters are generated, including: normalizing the user's mobile phone signaling data to obtain a sample set corresponding to the user; determining all core objects in the sample set according to the adjusted neighborhood parameter values; for each core object in the sample set, finding density-reachable samples in the sample set to generate clusters corresponding to each core object.

[0074] Specifically, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) can be used to cluster the sample set.

[0075] According to the adjusted neighborhood parameter values, all core objects are determined in the sample set. For example, first, the location information and access time in the mobile phone signaling data are normalized; the location information includes the longitude and latitude, and the sample set D is obtained: ((dlon1, dlat1, dt1), ..., (dlon m ,dlat m ,dt m )), where (dlon m ,dlat m ,dt m ) is the mth signaling data. m Indicates the longitude position of the base station corresponding to the signaling in the mth signaling sample. m Indicates the latitude position of the base station corresponding to the signaling in the mth signaling sample. m Characterize the access time of the signaling in the mth signaling sample. Initialize the core object set to an empty set. Determine the neighborhood of each signaling sample in the sample set, for example, determine the sample (dlon) by the neighborhood parameter value of the jth base station. j ,dlat j ,dt j )'s neighborhood The value of j is 1, 2, ..., m. If the sample (dlon j ,dlat j ,dt j ) is greater than or equal to the preset minimum number of core object sample points MinPts, then the sample (dlon j ,dlat j ,dt j ) is added to the core object set. The minimum sample points MinPts of the core object can be set according to actual needs.

[0076] For each core object in the sample set, perform the following operations in sequence to obtain the clusters corresponding to each core object: first, initialize the number of clusters K, where the initial value of the number of clusters k is 0, and add 1 to the number of clusters k each time a cluster corresponding to a core object is obtained. Then initialize the unvisited sample set, where each time a cluster corresponding to a core object is obtained, delete the samples in the cluster corresponding to the newly generated core object in the sample set to update the sample set. When the core object set is not an empty set, initialize an empty queue Q, then randomly select a core object from the core object set and add it to the queue Q, and take the core object out of the queue Q, for example (dlon q ,dlat q ,dt q ), through the core object (dlon q,dlat q ,dt q ) The neighborhood parameter value corresponding to the base station determines the core object (dlon q ,dlat q ,dt q )'s neighborhood If the core object (dlon q ,dlat q ,dt q ) is greater than or equal to the preset minimum number of core object sample points MinPts, then the core object (dlon q ,dlat q ,dt q )'s neighbors are added to queue Q to form a core object (dlon q ,dlat q ,dt q ) corresponding to the clusters.

[0077] Step S104: taking the geometric center of the cluster as the residence point.

[0078] Specifically, the geometric center can be obtained by calculating the average coordinates of all points in the cluster.

[0079] Step S105 , subtracting the minimum access time in the cluster from the maximum access time in the cluster to obtain the residence time of the residence point.

[0080] Specifically, when there are multiple clusters, each cluster corresponds to a residence point, and the residence time of the residence point corresponding to each cluster can be obtained by subtracting the minimum access time in the corresponding cluster from the maximum access time in each cluster.

[0081] The method for determining the dwell point provided in the present application obtains the user's mobile phone signaling data and base station density, and then determines the core object in the mobile phone signaling data according to the base station density, thereby generating a cluster, and finally determining the geometric center of the cluster as the dwell point. In this way, the influence of the base station density on the mobile phone signaling data can be considered, and the mobile phone signaling data can be clustered based on the base station density, so as to achieve a more accurate effect of determining the dwell point.

[0082] On the basis of Example 1, in order to further improve the accuracy of determining the dwell point, this embodiment provides a method for obtaining mobile phone signaling data, as shown in FIG3(a), the method includes:

[0083] Step S201, obtaining full data, wherein the full data includes all signaling data of the user within a preset time period.

[0084] Specifically, global data may be obtained from a preset database, the global data including all signaling data of all users within a preset time period, and the global data may be grouped according to the identity information of the user, thereby obtaining the full amount of data of each user.

[0085] Step S202: clean all the data.

[0086] Specifically, the full amount of data is cleaned, including: sorting multiple signaling data in the full amount of data according to the access time; then calculating the distance and time difference between adjacent signaling data to obtain the moving speed of the next signaling data; and finally deleting the signaling data whose moving speed is greater than a preset speed threshold from the full amount of data. In some embodiments, the preset speed threshold is, for example, 200 km / h. If the moving speed of the signaling data is greater than the preset speed threshold, it can be determined that the signaling data is abnormal, and thus the signaling data needs to be removed from the full amount of data.

[0087] In some embodiments, before sorting the plurality of signaling data in the full amount of data according to the access time, it also includes: removing the signaling data with missing key attributes and duplicates from the full amount of data. Specifically, the key attributes of the signaling data include: the identity information of the user, the identification code of the mobile device, the timestamp corresponding to the signaling data, the identification code of the base station corresponding to the signaling data, and the location information of the base station corresponding to the signaling data.

[0088] Step S203, performing data filling and / or data simplification on the cleaned full amount of data to obtain the user's mobile phone signaling data.

[0089] The cleaned full data is filled with data, including: sorting the signaling data in the cleaned full data according to the access time, and if the time difference between adjacent signaling data is greater than the first preset time threshold, and corresponds to the same base station, then filling data between adjacent signaling data according to the first preset time interval. In this way, when the base station density is relatively sparse, the signaling data can be filled with data to balance the base station density and reduce the error caused by the sparse base station density. Specifically, the first preset time interval is, for example, 36 seconds.

[0090] The data simplification of the cleaned full data includes: after sorting the signaling data in the cleaned full data according to the access time, grouping the sorted signaling data according to the second preset time interval, determining whether the time difference between adjacent signaling data is greater than the second preset time threshold in each time interval group, if the time difference between adjacent signaling data in the time interval group is less than or equal to the second preset time threshold, determining the frequency of occurrence of the signaling data relative to the base station, retaining the signaling data with the highest frequency in the time interval group, and deleting other signaling data in the time interval group. In some embodiments, the second preset time interval is, for example, 3 minutes.

[0091] Further, determining the occurrence frequency of signaling data relative to the base station includes: grouping the signaling data in the time interval group according to the base station identification code, calculating the frequency of the signaling data in each base station identification code group, and obtaining the frequency of the signaling data corresponding to each base station.

[0092] In this way, when the time difference between adjacent signaling data in the time interval group is less than or equal to the second preset time threshold, it can be determined that the mobile device is in an area where base stations are densely distributed. By simplifying the data of the cleaned full amount of data, the amount of signaling data can be reduced, thereby simplifying the complexity of mobile phone signaling data clustering. In addition, by retaining the signaling data with the highest frequency in each time interval group and deleting other signaling data, it is possible to avoid determining the wrong residence point from other signaling data, thereby reducing the probability of the occurrence of the wrong residence point.

[0093] The method for obtaining mobile phone signaling data provided in this embodiment performs data cleaning on the full amount of data, that is, removes duplicate signaling data and signaling data with missing key attributes in the full amount of data. In this way, the influence of non-compliant data on the determination result of the residence point can be avoided, thereby improving the accuracy of determining the residence point. In addition, after sorting the signaling data in the cleaned full amount of data according to the access time, if the time difference between adjacent signaling data is greater than the first preset time threshold, and corresponds to the same base station, data is filled between adjacent signaling data according to the first preset time interval. In the case where the time difference between adjacent signaling data is greater than the first preset time threshold, and corresponds to the same base station, it can be determined that the mobile device is in an area with a sparse number of base stations, and by filling data between adjacent signaling data, the sparsity of the signaling data can be reduced, thereby achieving a better clustering effect. In addition, after the sorted signaling data is grouped according to the second preset time interval, if the time difference between adjacent signaling data in the time interval group is less than or equal to the second preset time threshold, the frequency of occurrence of the signaling data relative to the base station is determined, and the signaling data with the highest frequency of occurrence is retained in the time interval group, and other signaling data in the time interval group is deleted. In this way, in the case where the time difference between adjacent signaling data in the time interval group is less than or equal to the second preset time threshold, it can be determined that the mobile device is in an area with a relatively dense number of base stations, by retaining the signaling data with the highest frequency of occurrence in the time interval group and deleting other signaling data in the time interval group. In this way, the signaling data can be reduced, the complexity of signaling data clustering can be reduced, and at the same time, the interference of other signaling data in the time interval group on the determination of the dwelling point can be reduced, thereby achieving the effect of improving the accuracy of determining the dwelling point.

[0094] As shown in FIG3(b), the present embodiment provides a schematic diagram of the principle of a method for determining a dwelling point. The principle of the method for determining a dwelling point mainly includes four steps. The first step is data preprocessing S310, thereby obtaining the user's mobile phone signaling data. The second step is base station density calculation S320, the third step is to cluster user trajectories in the user's mobile phone signaling data according to the base station density S330 to form clusters, and the fourth step is to mark the dwelling point at the geometric center of the cluster S340. The data preprocessing S310 includes four steps, namely data extraction S311, user grouping S312, data cleaning S313 and data filling S314. Data extraction S311 is to obtain global data, and then perform user grouping on the global data. User grouping S312 is to group the global data according to the user's identity information, thereby obtaining the full amount of data for each user. Then, the full amount of data is cleaned. Data cleaning S313 is to remove signaling data with missing key attributes, duplicate signaling data, and signaling data with a moving speed greater than a preset speed threshold in the full amount of data. Then, data filling is performed on the full amount of data after data cleaning. Data filling S314 is to sort the signaling data in the cleaned full amount of data according to the access time. When the time difference between adjacent signaling data is greater than the first preset time threshold and corresponds to the same base station, data filling is performed between adjacent signaling data according to the first preset time interval.

[0095] Figure 4 This is a schematic diagram of the structure of the device for determining the dwell point provided in this application. Figure 4 As shown, the device 400 for determining the dwelling point provided in this embodiment includes: a first acquisition module 410, a second acquisition module 420, a user trajectory clustering module 430 and a dwelling information marking module 440. The first acquisition module 410 is used to obtain the user's mobile phone signaling data; the mobile phone signaling data includes user identity information, the access time of the signaling and the location information of the base station corresponding to the signaling, and the second acquisition module 420 is used to obtain the base station density. The user trajectory clustering module 430 is used to determine the core object in the user's mobile phone signaling data according to the base station density and generate a clustering cluster. The resident information marking module 440 is used to use the geometric center in the clustering cluster as the dwelling point.

[0096] In a possible implementation, the first acquisition module 410 includes a data extraction unit 411, a data cleaning unit 412, and a data filling unit 413. The data extraction unit 411 is used to obtain full data, and the full data includes all signaling data of the user within a preset time period. The data cleaning unit 412 is used to clean the full data. The data filling unit 413 is used to fill and / or simplify the cleaned full data to obtain the user's mobile phone signaling data.

[0097] In one possible implementation, the data cleaning unit 412 is used to sort multiple signaling data in the full data according to access time; calculate the distance and time difference between adjacent signaling data to obtain the moving speed of the next signaling data; and delete the signaling data whose moving speed is greater than a preset speed threshold from the full data.

[0098] In a possible implementation, the data filling unit 413 is used to sort the signaling data in the cleaned full data according to the access time. If the time difference between adjacent signaling data is greater than a first preset time threshold and corresponds to the same base station, data filling is performed between adjacent signaling data according to the first preset time interval.

[0099] In a possible implementation, the second acquisition module 420 is used to acquire the number of base stations in at least one preset area. The weights corresponding to the preset areas are determined according to the distance between the boundary of each preset area and the target base station, the weight is inversely proportional to the distance, and the target base station is any base station in the preset area. The base station density of the target base station is determined based on the weights and the number of base stations.

[0100] In a possible implementation, the user trajectory clustering module 430 is used to adjust the preset neighborhood parameter value based on the base station density, find all core objects in the user's mobile phone signaling data according to the adjusted neighborhood parameter value, and generate clusters.

[0101] In a possible implementation, the user trajectory clustering module 430 is used to normalize the user's mobile phone signaling data to obtain a sample set corresponding to the user. All core objects are determined in the sample set according to the adjusted neighborhood parameter value. For each core object in the sample set, a sample with a density reachable therefrom is found in the sample set to generate a cluster corresponding to each core object.

[0102] In a possible implementation, the residence information marking module 440 is further configured to obtain the residence time of the residence point by subtracting the minimum access time in the cluster from the maximum access time in the cluster.

[0103] The device for determining a dwell point provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effect are similar, which will not be described in detail in this embodiment.

[0104] Figure 5 This is a schematic diagram of the structure of the electronic device provided in this application. Figure 5As shown, the electronic device 500 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus.

[0105] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that at least one processor 501 executes the above method.

[0106] The specific implementation process of the processor 501 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0107] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0108] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0109] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0110] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0111] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0112] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0113] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0114] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0115] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0116] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0117] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0118] A person skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0119] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for determining a dwell point, characterized in that: include: Obtain the user's mobile phone signaling data; The mobile phone signaling data includes user identity information, signaling access time and location information of the base station corresponding to the signaling; Get base station density; Determine core objects in the mobile phone signaling data of the user according to the base station density, and generate clusters; The geometric center of the cluster is taken as the residence point.

2. The method according to claim 1, characterized in that The obtaining of the user's mobile phone signaling data includes: Acquire full data, where the full data includes all signaling data of the user within a preset time period; Performing data cleaning on the full amount of data; The cleaned full amount of data is filled with data and / or simplified to obtain the mobile phone signaling data of the user.

3. The method according to claim 2, characterized in that The data cleaning of the full amount of data includes: sorting the plurality of signaling data in the full amount of data according to access time; Calculate the distance and time difference between adjacent signaling data to obtain the moving speed of the next signaling data; The signaling data with a moving speed greater than a preset speed threshold is deleted from the full amount of data.

4. The method according to claim 2, characterized in that: The step of filling the cleaned full amount of data includes: Sort the signaling data in the cleaned full data according to the access time; If the time difference between adjacent signaling data is greater than a first preset time threshold and corresponds to the same base station, data filling is performed between the adjacent signaling data according to a first preset time interval.

5. The method according to claim 1, characterized in that The obtaining of base station density includes: Obtaining the number of base stations in at least one preset area; Determine the weights corresponding to the preset areas according to the distances between the boundaries of the preset areas and the target base station, wherein the weights are inversely proportional to the distances, and the target base station is any base station within the preset areas; The base station density of the target base station is determined according to each of the weights and the number of each of the base stations.

6. The method according to claim 1, characterized in that The determining of core objects in the mobile phone signaling data of the user according to the base station density and generating clusters includes: Adjusting a preset neighborhood parameter value based on the base station density; All core objects are found in the mobile phone signaling data of the user according to the adjusted neighborhood parameter values, and clusters are generated.

7. The method according to claim 6, characterized in that The step of finding all core objects in the user's mobile phone signaling data according to the adjusted neighborhood parameter value and generating clusters includes: Normalizing the mobile phone signaling data of the user to obtain a sample set corresponding to the user; Determine all core objects in the sample set according to the adjusted neighborhood parameter values; For each core object in the sample set, find the samples whose density is reachable in the sample set, and generate clusters corresponding to each core object.

8. The method according to any one of claims 1 to 7, characterized in that: After taking the geometric center of the cluster as the stationary point, the method further includes: The residence time of the residence point is obtained by subtracting the minimum access time in the cluster from the maximum access time in the cluster.

9. A device for determining a dwell point, characterized in that: include: A first acquisition module, used to acquire the user's mobile phone signaling data; The mobile phone signaling data includes user identity information, signaling access time and location information of the base station corresponding to the signaling; A second acquisition module is used to acquire base station density; A user trajectory clustering module, used to determine core objects in the mobile phone signaling data of the user according to the base station density, and generate clusters; The resident information marking module is used to use the geometric center of the cluster as the resident point.

10. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 8 when executed by a processor.

12. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 8 when being executed by a processor.

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